Quant Bootcamp

Effective Data Visualization - Why

• Data visualization is not a tool, it is a means for communicating results
• The communicator needs to bring the story visually and contextually to life
• Requires skill to make graphs that conveys the in an effective manner
• You want to make the entire audience take away the same thing from your visualization
• To understand what effective visualizations are, let us take a look at some examples which are not effective

Say no to 3D graphs!

The more, the merrier?

Misleading Visualization

Avoid Pie Charts

Always a zero baseline

Data Visualization in R

What?

• Converting large amounts of data into easily understood information
• This graphical representation of data is data visualization

Why?

• While exploring your data, you would want to see associations between variables to understand patterns in data
• For effective presentation of data

The ggplot2 package

Content for this topic has been sourced from Winston Chang’s ‘R Graphics Cookbook, 2nd edition’. Please check out the work for detailed information.

Loading the packages

• tidyverse is a collection of some R packages
• If you load tidyverse, no need to load ggplot2 separately

library(tidyverse)
library(ISLR2)

• Load the diamonds dataset from ggplot2

ggplot2 Terminology

Content for this topic has been sourced from Winston Chang’s ‘R Graphics Cookbook, 2nd edition’. Please check out his work for detailed information.

Some of the terminologies used in ggplot2:

• data- what we want to visualize and consists of variables
• Geoms - geometric objects that are drawn to represent the data, such as bars, lines, and points
• aesthetics - visual properties of geoms, such as x and y position, line color, point shapes, etc
• There are mappings from data values to aesthetics

Building a plot

Task - You want to plot a boxplot of cut and depth

ggplot(data = diamonds) #your plot area

Building a plot

Task - You want to plot a boxplot of cut and depth

ggplot(data = diamonds) + # your plot area
  geom_boxplot(aes(cut, depth),
               fill = 
                 "lightyellow") #your geom and aesthetic

Building a plot

Task - You want to plot a boxplot of cut and depth

ggplot(data = diamonds) + # your plot area
  geom_boxplot(aes(cut, depth),
               fill = "lightyellow") + #your geom and aesthetic
  theme_minimal() #adding the theme

Building a plot

Task - You want to plot a boxplot of cut and depth

ggplot(data = diamonds) + # your plot area
  geom_boxplot(aes(cut, depth),
               fill = "lightyellow") + #your geom and aesthetic
  theme_minimal() + #adding the theme
  labs(x = "Cut of Diamonds",
    y = "Depth",
    title = "Total Depth Percentage by Cut"
    )# labels

A Graphing Template

Content for this topic has been sourced from Hadley Wickham’s ‘R for Data Science’. Please check out his work for detailed information.

• A basic template for plotting a graph through ggplot2 can be

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Plotting a….

ggplot(data = diamonds) +
  geom_histogram(aes(x = carat))

ggplot(data = diamonds) +
  geom_bar(aes(x = cut))

Scatterplots - Plotting two continuous variables

• Load the Auto dataset from ISLR2 package

Task - You want to plot a scatterplot of mpg and displacement

ggplot(data = Auto) +
  geom_point(aes(x = mpg, y = displacement)) 

Aesthetics

Auto$origin <- as.factor(Auto$origin)
  
ggplot(data = Auto) +
  geom_point(aes(x = mpg, y = displacement,
                 shape = origin)) #shows different origin by shapes

ggplot(data = Auto) +
  geom_point(aes(x = mpg, y = displacement,
                 colour = origin)) #shows different origins by colours

ggplot(data = Auto) +
  geom_point(aes(x = mpg, y = displacement,
                 shape = origin),
             alpha = 0.5) #for setting the opacity

Bar Charts - Plotting a discrete and a continuous variable

Task - You want to plot the mean number of carat by cut from diamonds dataset

diamonds |>
  group_by(cut) |>
  summarise(mean_carat = mean(carat)) |>
  ggplot(aes(x = cut, y = mean_carat))+
  geom_col()

Formatting your Plor

p1 <- diamonds |>
  group_by(cut) |>
  summarise(mean_carat = mean(carat)) |>
  ggplot(aes(x = cut, y = mean_carat))+
  geom_col(width = 0.70, fill = "#9ac5db")

p1

p2 <- diamonds |>
  group_by(cut) |>
  summarise(mean_carat = mean(carat)) |>
  ggplot(aes(x = reorder(cut, mean_carat), y = mean_carat))+
  geom_col(width = 0.70, fill = "#9ac5db")

p2

p3 <- diamonds |>
  group_by(cut) |>
  summarise(mean_carat = mean(carat)) |>
  ggplot(aes(x = reorder(cut, mean_carat), y = mean_carat))+
  geom_col(width = 0.70, fill = "#9ac5db")+
  labs(title = "'Fair' Cut Has the Highest Mean Carat Value",
       subtitle = "Mean Carat Value by Quality of the Cut",
       y = "Mean Carat",
       x = "Quality of the Cut",
       caption = "Data Source : diamonds | Analysis by Particpant")

p3

p4 <- diamonds |>
  group_by(cut) |>
  summarise(mean_carat = mean(carat)) |>
  ggplot(aes(x = reorder(cut, mean_carat), y = mean_carat))+
  geom_col(width = 0.70, fill = "#9ac5db")+
  labs(title = "'Fair' Cut Has the Highest Mean Carat Value",
       subtitle = "Mean Carat Value by Quality of the Cut",
       y = "Mean Carat",
       x = "Quality of the Cut",
       caption = "Data Source : diamonds | Analysis by Particpant")+
  coord_flip()+
  theme_minimal()

p4

Line Chart - Showing Trends Over Time

• Use the txhousing data from ggplot2

Patchwork - Combine your plots

• After making all your plots, you can combine them using patchwork

library(patchwork)

• It simply combines all your plots the way you want

p1 + p2 + p3+ p4

Patchwork - Combine your plots

• You can combine your plots in many ways

p1 / p4

• Annotations can be also added

my_patchwork <- (p1 + p2)/(p3 + p4)

my_patchwork + plot_annotation(
  title = 'How I formatted my plots',
  subtitle = 'I used patchwork to format my plots',
  caption = 'Author: Participant')